{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Copyright (C)                                                       \n",
    " 2016 - 2019 Pinard Liu(liujianping-ok@163.com) \n",
    " \n",
    " https://www.cnblogs.com/pinard\n",
    " \n",
    " Permission given to modify the code as long as you keep this declaration at the top                               \n",
    "\n",
    "用scikit-learn和pandas学习线性回归\n",
    "https://www.cnblogs.com/pinard/p/6016029.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets, linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read_csv里面的参数是csv在你电脑上的路径，此处csv文件放在notebook运行目录下面的CCPP目录里\n",
    "data = pd.read_csv('.\\CCPP\\ccpp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AT</th>\n",
       "      <th>V</th>\n",
       "      <th>AP</th>\n",
       "      <th>RH</th>\n",
       "      <th>PE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.34</td>\n",
       "      <td>40.77</td>\n",
       "      <td>1010.84</td>\n",
       "      <td>90.01</td>\n",
       "      <td>480.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.64</td>\n",
       "      <td>58.49</td>\n",
       "      <td>1011.40</td>\n",
       "      <td>74.20</td>\n",
       "      <td>445.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.74</td>\n",
       "      <td>56.90</td>\n",
       "      <td>1007.15</td>\n",
       "      <td>41.91</td>\n",
       "      <td>438.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.07</td>\n",
       "      <td>49.69</td>\n",
       "      <td>1007.22</td>\n",
       "      <td>76.79</td>\n",
       "      <td>453.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.80</td>\n",
       "      <td>40.66</td>\n",
       "      <td>1017.13</td>\n",
       "      <td>97.20</td>\n",
       "      <td>464.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      AT      V       AP     RH      PE\n",
       "0   8.34  40.77  1010.84  90.01  480.48\n",
       "1  23.64  58.49  1011.40  74.20  445.75\n",
       "2  29.74  56.90  1007.15  41.91  438.76\n",
       "3  19.07  49.69  1007.22  76.79  453.09\n",
       "4  11.80  40.66  1017.13  97.20  464.43"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取前五行数据，如果是最后五行，用data.tail()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AT</th>\n",
       "      <th>V</th>\n",
       "      <th>AP</th>\n",
       "      <th>RH</th>\n",
       "      <th>PE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9563</th>\n",
       "      <td>15.12</td>\n",
       "      <td>48.92</td>\n",
       "      <td>1011.80</td>\n",
       "      <td>72.93</td>\n",
       "      <td>462.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9564</th>\n",
       "      <td>33.41</td>\n",
       "      <td>77.95</td>\n",
       "      <td>1010.30</td>\n",
       "      <td>59.72</td>\n",
       "      <td>432.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9565</th>\n",
       "      <td>15.99</td>\n",
       "      <td>43.34</td>\n",
       "      <td>1014.20</td>\n",
       "      <td>78.66</td>\n",
       "      <td>465.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9566</th>\n",
       "      <td>17.65</td>\n",
       "      <td>59.87</td>\n",
       "      <td>1018.58</td>\n",
       "      <td>94.65</td>\n",
       "      <td>450.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9567</th>\n",
       "      <td>23.68</td>\n",
       "      <td>51.30</td>\n",
       "      <td>1011.86</td>\n",
       "      <td>71.24</td>\n",
       "      <td>451.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AT      V       AP     RH      PE\n",
       "9563  15.12  48.92  1011.80  72.93  462.59\n",
       "9564  33.41  77.95  1010.30  59.72  432.90\n",
       "9565  15.99  43.34  1014.20  78.66  465.96\n",
       "9566  17.65  59.87  1018.58  94.65  450.93\n",
       "9567  23.68  51.30  1011.86  71.24  451.67"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9568, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AT</th>\n",
       "      <th>V</th>\n",
       "      <th>AP</th>\n",
       "      <th>RH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.34</td>\n",
       "      <td>40.77</td>\n",
       "      <td>1010.84</td>\n",
       "      <td>90.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.64</td>\n",
       "      <td>58.49</td>\n",
       "      <td>1011.40</td>\n",
       "      <td>74.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.74</td>\n",
       "      <td>56.90</td>\n",
       "      <td>1007.15</td>\n",
       "      <td>41.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.07</td>\n",
       "      <td>49.69</td>\n",
       "      <td>1007.22</td>\n",
       "      <td>76.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.80</td>\n",
       "      <td>40.66</td>\n",
       "      <td>1017.13</td>\n",
       "      <td>97.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      AT      V       AP     RH\n",
       "0   8.34  40.77  1010.84  90.01\n",
       "1  23.64  58.49  1011.40  74.20\n",
       "2  29.74  56.90  1007.15  41.91\n",
       "3  19.07  49.69  1007.22  76.79\n",
       "4  11.80  40.66  1017.13  97.20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[['AT', 'V', 'AP', 'RH']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>445.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>438.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>453.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>464.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PE\n",
       "0  480.48\n",
       "1  445.75\n",
       "2  438.76\n",
       "3  453.09\n",
       "4  464.43"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data[['PE']]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7176, 4)\n",
      "(7176, 1)\n",
      "(2392, 4)\n",
      "(2392, 1)\n"
     ]
    }
   ],
   "source": [
    "print X_train.shape\n",
    "print y_train.shape\n",
    "print X_test.shape\n",
    "print y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linreg = LinearRegression()\n",
    "\n",
    "linreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 447.06297099]\n",
      "[[-1.97376045 -0.23229086  0.0693515  -0.15806957]]\n"
     ]
    }
   ],
   "source": [
    "print linreg.intercept_\n",
    "print linreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 20.0804012021\n",
      "RMSE: 4.48111606657\n"
     ]
    }
   ],
   "source": [
    "#模型拟合测试集\n",
    "y_pred = linreg.predict(X_test)\n",
    "from sklearn import metrics\n",
    "# 用scikit-learn计算MSE\n",
    "print \"MSE:\",metrics.mean_squared_error(y_test, y_pred)\n",
    "# 用scikit-learn计算RMSE\n",
    "print \"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 23.2089074701\n",
      "RMSE: 4.81756239919\n"
     ]
    }
   ],
   "source": [
    "X = data[['AT', 'V', 'AP']]\n",
    "y = data[['PE']]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train, y_train)\n",
    "#模型拟合测试集\n",
    "y_pred = linreg.predict(X_test)\n",
    "from sklearn import metrics\n",
    "# 用scikit-learn计算MSE\n",
    "print \"MSE:\",metrics.mean_squared_error(y_test, y_pred)\n",
    "# 用scikit-learn计算RMSE\n",
    "print \"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 20.7927937888\n",
      "RMSE: 4.55991159879\n"
     ]
    }
   ],
   "source": [
    "X = data[['AT', 'V', 'AP', 'RH']]\n",
    "y = data[['PE']]\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "predicted = cross_val_predict(linreg, X, y, cv=9)\n",
    "# 用scikit-learn计算MSE\n",
    "print \"MSE:\",metrics.mean_squared_error(y, predicted)\n",
    "# 用scikit-learn计算RMSE\n",
    "print \"RMSE:\",np.sqrt(metrics.mean_squared_error(y, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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fwZ/vx+z7WIJShVyHyFaM+Roiyzt7GrMEkQvpCzJpbr45YgSEQxAUAUcVot2f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\n4orCcRzHyYkrCsdxHCcnrigcx3GcnPw/BVOaDlMF3+MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x93da850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y, predicted)\n",
    "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n",
    "ax.set_xlabel('Measured')\n",
    "ax.set_ylabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
